'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A_     :						List[str]    							=logging.get_logger(__name__)

A_     :						Dict    							={
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							Tuple	,   __snake_case  :							str	,   __snake_case  :							Optional[int])			->					Optional[int]:






   for attribute in key.split('''.'''):
      lowerCAmelCase_			=      getattr(UpperCamelCase_	,   UpperCamelCase_)

   if weight_type is not None:
      lowerCAmelCase_			=      getattr(UpperCamelCase_	,   UpperCamelCase_).shape
   else:
      lowerCAmelCase_			=      hf_pointer.shape

   assert hf_shape == value.shape, (
       F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
       F''' {value.shape} for {full_name}'''
   )

   if weight_type == "weight":
      lowerCAmelCase_			=      value
   elif weight_type == "weight_g":
      lowerCAmelCase_			=      value
   elif weight_type == "weight_v":
      lowerCAmelCase_			=      value
   elif weight_type == "bias":
      lowerCAmelCase_			=      value
   else:
      lowerCAmelCase_			=      value

   logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''')



def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Tuple	,   __snake_case  :							List[str])			->					Optional[Any]:
   lowerCAmelCase_			=      []
   lowerCAmelCase_			=      fairseq_model.state_dict()

   lowerCAmelCase_			=      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowerCAmelCase_			=      False
      if "conv_layers" in name:
         load_conv_layer(
             UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_	,   hf_model.config.feat_extract_norm == '''group'''	,   )
         lowerCAmelCase_			=      True
      else:
         for key, mapped_key in MAPPING.items():
            lowerCAmelCase_			=      '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

            if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
               lowerCAmelCase_			=      True
               if "*" in mapped_key:
                  lowerCAmelCase_			=      name.split(UpperCamelCase_)[0].split('''.''')[-2]
                  lowerCAmelCase_			=      mapped_key.replace('''*'''	,   UpperCamelCase_)
               if "weight_g" in name:
                  lowerCAmelCase_			=      '''weight_g'''
               elif "weight_v" in name:
                  lowerCAmelCase_			=      '''weight_v'''
               elif "weight" in name:
                  lowerCAmelCase_			=      '''weight'''
               elif "bias" in name:
                  lowerCAmelCase_			=      '''bias'''
               else:
                  lowerCAmelCase_			=      None
               set_recursively(UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_)
            continue
      if not is_used:
         unused_weights.append(UpperCamelCase_)

   logger.warning(F'''Unused weights: {unused_weights}''')



def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							List[Any]	,   __snake_case  :							Optional[int]	,   __snake_case  :							List[str]	,   __snake_case  :							Union[str, Any])			->					Dict:
   lowerCAmelCase_			=      full_name.split('''conv_layers.''')[-1]
   lowerCAmelCase_			=      name.split('''.''')
   lowerCAmelCase_			=      int(items[0])
   lowerCAmelCase_			=      int(items[1])

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
         )
         lowerCAmelCase_			=      value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
         )
         lowerCAmelCase_			=      value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
             " found."
         )
         lowerCAmelCase_			=      value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
         )
         lowerCAmelCase_			=      value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
   else:
      unused_weights.append(UpperCamelCase_)



def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple)			->					Tuple:
   lowerCAmelCase_			=      SEWConfig()
   if is_finetuned:
      lowerCAmelCase_			=      model.wav_encoder.wav_model.cfg
   else:
      lowerCAmelCase_			=      model.cfg

   lowerCAmelCase_			=      fs_config.conv_bias
   lowerCAmelCase_			=      eval(fs_config.conv_feature_layers)
   lowerCAmelCase_			=      [x[0] for x in conv_layers]
   lowerCAmelCase_			=      [x[1] for x in conv_layers]
   lowerCAmelCase_			=      [x[2] for x in conv_layers]
   lowerCAmelCase_			=      '''gelu'''
   lowerCAmelCase_			=      '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   lowerCAmelCase_			=      0.0
   lowerCAmelCase_			=      fs_config.activation_fn.name
   lowerCAmelCase_			=      fs_config.encoder_embed_dim
   lowerCAmelCase_			=      0.0_2
   lowerCAmelCase_			=      fs_config.encoder_ffn_embed_dim
   lowerCAmelCase_			=      1E-5
   lowerCAmelCase_			=      fs_config.encoder_layerdrop
   lowerCAmelCase_			=      fs_config.encoder_attention_heads
   lowerCAmelCase_			=      fs_config.conv_pos_groups
   lowerCAmelCase_			=      fs_config.conv_pos
   lowerCAmelCase_			=      len(UpperCamelCase_)
   lowerCAmelCase_			=      fs_config.encoder_layers
   lowerCAmelCase_			=      fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      lowerCAmelCase_			=      model.cfg
      lowerCAmelCase_			=      fs_config.final_dropout
      lowerCAmelCase_			=      fs_config.layerdrop
   lowerCAmelCase_			=      fs_config.activation_dropout
   lowerCAmelCase_			=      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   lowerCAmelCase_			=      fs_config.attention_dropout
   lowerCAmelCase_			=      fs_config.dropout_input
   lowerCAmelCase_			=      fs_config.dropout
   lowerCAmelCase_			=      fs_config.mask_channel_length
   lowerCAmelCase_			=      fs_config.mask_channel_prob
   lowerCAmelCase_			=      fs_config.mask_length
   lowerCAmelCase_			=      fs_config.mask_prob

   lowerCAmelCase_			=      '''Wav2Vec2FeatureExtractor'''
   lowerCAmelCase_			=      '''Wav2Vec2CTCTokenizer'''

   return config



@torch.no_grad()
def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							List[str]	,   __snake_case  :							str=None	,   __snake_case  :							int=None	,   __snake_case  :							List[str]=True)			->					Optional[Any]:






   if is_finetuned:
      lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1])})
   else:
      lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

   if config_path is not None:
      lowerCAmelCase_			=      SEWConfig.from_pretrained(UpperCamelCase_)
   else:
      lowerCAmelCase_			=      convert_config(model[0]	,   UpperCamelCase_)
   lowerCAmelCase_			=      model[0].eval()

   lowerCAmelCase_			=      True if config.feat_extract_norm == '''layer''' else False
   lowerCAmelCase_			=      WavaVecaFeatureExtractor(
       feature_size=1	,   sampling_rate=16000	,   padding_value=0	,   do_normalize=UpperCamelCase_	,   return_attention_mask=UpperCamelCase_	,   )

   if is_finetuned:
      if dict_path:
         lowerCAmelCase_			=      Dictionary.load(UpperCamelCase_)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         lowerCAmelCase_			=      target_dict.pad_index
         lowerCAmelCase_			=      target_dict.bos_index
         lowerCAmelCase_			=      target_dict.pad_index
         lowerCAmelCase_			=      target_dict.bos_index
         lowerCAmelCase_			=      target_dict.eos_index
         lowerCAmelCase_			=      len(target_dict.symbols)
         lowerCAmelCase_			=      os.path.join(UpperCamelCase_	,   '''vocab.json''')
         if not os.path.isdir(UpperCamelCase_):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_))
            return
         os.makedirs(UpperCamelCase_	,   exist_ok=UpperCamelCase_)
         with open(UpperCamelCase_	,   '''w'''	,   encoding='''utf-8''') as vocab_handle:
            json.dump(target_dict.indices	,   UpperCamelCase_)
         lowerCAmelCase_			=      WavaVecaCTCTokenizer(
             UpperCamelCase_	,   unk_token=target_dict.unk_word	,   pad_token=target_dict.pad_word	,   bos_token=target_dict.bos_word	,   eos_token=target_dict.eos_word	,   word_delimiter_token='''|'''	,   do_lower_case=UpperCamelCase_	,   )
         lowerCAmelCase_			=      WavaVecaProcessor(feature_extractor=UpperCamelCase_	,   tokenizer=UpperCamelCase_)
         processor.save_pretrained(UpperCamelCase_)

      lowerCAmelCase_			=      SEWForCTC(UpperCamelCase_)
   else:
      lowerCAmelCase_			=      SEWModel(UpperCamelCase_)
      feature_extractor.save_pretrained(UpperCamelCase_)

   recursively_load_weights(UpperCamelCase_	,   UpperCamelCase_	,   UpperCamelCase_)

   hf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
 A_     :						Any    							=argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 A_     :						str    							=parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )









import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''laion/clap-htsat-unfused'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return RobertaTokenizer.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							ClapProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ClapProcessor(tokenizer=self.get_tokenizer()     ,   feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''       )
         snake_case											=							self.get_feature_extractor(do_normalize=__snake_case     ,   padding_value=1.0       )

         snake_case											=							ClapProcessor.from_pretrained(
             self.tmpdirname     ,   bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''     ,   do_normalize=__snake_case     ,   padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							floats_list((3, 1_0_0_0)       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audios=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							'''This is a test string'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names[2:]     ,   feature_extractor.model_input_names     ,   msg='''`processor` and `feature_extractor` model input names do not match'''     ,   )




